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The experiments on CASIA show that despite neglecting detailed texture information, our method still provides results comparable to those of recent methods.
While the RMSE performance generally degrades as the number of distorted subbands increases, the proposed method still provides robust DOA estimation in all the three considered cases.
With vast amounts of omics data generated, the method still provides a new perspective for the future disease association studies.
Nevertheless, the method still provides valuable epidemiological data and proves extremely useful for prioritising isolates for further downstream analyses.
Though these probes only represent a sparse sampling on every transcript in the transcriptome, the method still provides reasonably accurate results.
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We demonstrated with a simple case that even when considering a WSR, our proposed method still provided a higher weighted throughput than when using BD.
Even so, this less complex analytic method still provided genotyping calls for >90% of the B>M probes and these calls were ∼95% consistent with independently determined genotypes.
Despite these differences, the LD method still provided useful information about N e for simulations of the scenario D life history (downwards bias of 15.4% for random samples of adults).
At a 10% mixture weight, the quantitative methods still provide positive identification information (blue, green).
Despite increases in read lengths and sequencing depths, these methods still provide little resolution of the fine-grained diversity at lower taxonomic ranks.
However, both of those methods still provide no information for assessing treatment effects and disease evolution from a molecular and cellular perspective.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com